Develop a learning path for data engineering on Google Cloud Platform (GCP). The graph should cover BigQuery for data warehousing, Dataflow for data processing pipelines, and Pub/Sub for messaging.
Developing a robust learning path for data engineering on Google Cloud Platform (GCP) requires a structured approach, building from foundational concepts to specialized services and their integration, specifically focusing on BigQuery for data warehousing, Dataflow for data processing pipelines, and Pub/Sub for messaging. This path incorporates current information as of late 2025, emphasizing hands-on experience and certification preparation.
Key Facts:
- BigQuery is GCP's fully managed, serverless enterprise data warehouse designed for scalable analysis of petabytes of data using standard SQL.
- Dataflow, based on Apache Beam, is a fully managed service for processing both streaming and batch data, simplifying the development of complex ETL/ELT pipelines.
- Cloud Pub/Sub is a fully managed asynchronous messaging service enabling reliable communication and real-time event data ingestion.
- A common real-time streaming pattern involves ingesting data via Pub/Sub, processing and transforming it using Dataflow, and then loading it into BigQuery for analysis.
- The learning path should heavily reinforce practical exercises and preparation for the Google Cloud Professional Data Engineer certification.
BigQuery for Data Warehousing
This sub-topic provides a deep dive into BigQuery, GCP's fully managed, serverless enterprise data warehouse. It covers its architecture, SQL capabilities, data loading, optimization techniques, and advanced features like BigQuery ML, crucial for scalable data analysis.
Key Facts:
- BigQuery is GCP's fully managed, serverless enterprise data warehouse.
- It is designed for scalable analysis of petabytes of data using standard SQL.
- Core concepts include its serverless architecture, separation of compute and storage, and columnar storage model.
- Practical skills involve writing efficient SQL queries, optimizing query performance, and various data loading techniques (batch and streaming).
- BigQuery ML allows building and deploying machine learning models directly using SQL.
BigQuery Architecture and Core Concepts
BigQuery's architecture is distinguished by its serverless model, separating compute and storage layers, and leveraging columnar storage with the Dremel query engine. This design allows for independent scaling and efficient analytical processing of petabytes of data without requiring users to manage infrastructure.
Key Facts:
- BigQuery's serverless architecture eliminates the need for users to manage infrastructure, provisioning, scaling, or maintenance.
- Data is stored in a columnar format (Google's Capacitor) optimized for efficient reading of large amounts of structured data.
- The separation of compute and storage layers enables independent scaling and resource allocation, enhancing flexibility and cost control.
- BigQuery leverages Dremel, Google's distributed query engine, for rapid query performance over petabyte-scale datasets.
- The columnar storage model allows BigQuery to read only necessary columns for a query, saving time and resources.
BigQuery Data Loading Techniques
BigQuery offers diverse data loading methods, including batch processing for large, non-real-time data volumes and streaming ingestion for low-latency, real-time analytics. These techniques are critical for getting data into the warehouse efficiently.
Key Facts:
- Batch loading is suitable for large volumes of data processed offline, typically using formats like CSV, JSON, or Parquet.
- Data for batch loading is often uploaded to Google Cloud Storage (GCS) before being loaded into BigQuery.
- The BigQuery Data Transfer Service (DTS) can automate scheduled batch transfers from various sources.
- Streaming ingestion enables real-time or near real-time analytics with low latency.
- Data can be streamed directly into BigQuery tables using the BigQuery Streaming API, often with Pub/Sub or Dataflow.
BigQuery ML
BigQuery ML (BQML) is an advanced feature that allows users to build and deploy machine learning models directly within BigQuery using standard SQL queries. This capability democratizes ML by enabling data analysts to leverage BigQuery's processing power without extensive programming or data movement.
Key Facts:
- BigQuery ML enables building and deploying machine learning models directly within BigQuery using SQL.
- It democratizes ML, allowing data analysts to leverage BigQuery's processing capabilities without extensive programming knowledge.
- BQML eliminates the need to move data to separate platforms for model training.
- It integrates with Vertex AI for comprehensive model management and deployment.
- Users can perform tasks like forecasting, classification, and clustering using SQL commands.
BigQuery Schema Design Principles
Effective schema design in BigQuery is paramount for optimizing performance and managing costs. This involves careful consideration of denormalization, leveraging nested and repeated fields, selecting appropriate data types, and defining schemas explicitly.
Key Facts:
- BigQuery often performs better with denormalized data, reducing the need for costly JOIN operations.
- Nested and repeated fields allow for logical grouping of related data, leading to more manageable queries and reduced costs.
- Choosing appropriate data types for columns is essential for storage optimization and query speed.
- Explicitly defining schemas is generally preferred over auto-inferring schemas to avoid potential data type mismatch errors.
- Normalization should only be considered if it significantly reduces dataset size or is essential for data consistency.
BigQuery SQL Capabilities and Query Optimization
BigQuery supports standard SQL, but effective query writing and optimization techniques are crucial for maximizing performance and managing costs. These methods include targeted SQL, partitioning, clustering, denormalization, and using materialized views.
Key Facts:
- BigQuery supports standard SQL, making it accessible for users familiar with traditional database languages.
- Writing efficient SQL queries, such as avoiding `SELECT *` and selecting only necessary columns, significantly reduces scanned data and improves performance.
- Partitioning divides large tables into smaller segments, drastically improving query performance by limiting data scanned.
- Clustering organizes data within partitions, optimizing retrieval for specific filtering conditions.
- Denormalization often performs better in BigQuery due to its columnar storage, reducing the need for costly `JOIN` operations.
- Materialized views store pre-computed results for frequently executed queries, reducing recomputation time.
Dataflow for Data Processing Pipelines
This sub-topic covers Google Cloud Dataflow, a fully managed service based on Apache Beam, designed for processing both streaming and batch data. It emphasizes designing and implementing complex ETL/ELT pipelines and leveraging Dataflow's capabilities for large-scale data transformation.
Key Facts:
- Dataflow is a fully managed service based on Apache Beam for processing streaming and batch data.
- It simplifies the development of complex ETL/ELT pipelines.
- A deep understanding of Apache Beam's programming model (PCollections, transforms, Runners) is fundamental.
- Dataflow supports both batch and streaming data processing, including windowing and triggers for advanced scenarios.
- Practical skills involve developing, deploying, and monitoring Dataflow jobs using Apache Beam SDKs and Google-provided templates.
Apache Beam Programming Model
The Apache Beam Programming Model is a unified, open-source framework for defining and executing data processing workflows, serving as the foundation for Google Cloud Dataflow. It provides a portable API allowing developers to create pipelines that can run on various processing engines, including Dataflow.
Key Facts:
- Apache Beam is an open-source, unified programming model for defining data processing workflows.
- It provides a portable API for constructing pipelines using SDKs in multiple languages (Java, Python, Go).
- Key concepts include Pipeline, PCollection (distributed dataset), PTransform (operations), and Runners (execution engines).
- Dataflow is a specific runner for Apache Beam pipelines on Google Cloud.
- PCollection can represent both bounded (batch) and unbounded (streaming) data.
Dataflow Templates
Dataflow Templates provide a streamlined method for packaging and deploying pipelines, separating pipeline design from execution. They allow for easy parameterization and deployment of common data processing tasks, with Google providing pre-built templates and the ability for users to create custom ones, particularly using Flex templates.
Key Facts:
- Dataflow templates package pipelines for easy deployment, decoupling design from execution.
- Google offers pre-built templates for common data processing tasks.
- Users can create custom templates, with Flex templates being the recommended modern approach.
- Templates can be parameterized, enabling customization during deployment without a full development environment.
- Flex templates package the pipeline as a Docker image for deployment.
Developing Data Processing Pipelines
Developing Data Processing Pipelines with Dataflow involves using Apache Beam SDKs to define the pipeline's logic, particularly for ETL/ELT scenarios, and then deploying these pipelines to the Dataflow service for managed execution.
Key Facts:
- Pipeline development utilizes Apache Beam SDKs, such as the Python SDK, to define processing logic.
- Dataflow is highly suitable for building complex ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) pipelines.
- Pipelines are deployed to the Dataflow service, which handles underlying infrastructure management.
- The defined pipeline outlines the entire data processing job from data ingestion to result output.
- The managed nature of Dataflow simplifies the operational aspects of pipeline execution.
Monitoring and Optimizing Dataflow Job Performance
Monitoring and Optimizing Dataflow Job Performance covers the essential practices for ensuring the efficiency and cost-effectiveness of Dataflow pipelines. This includes leveraging Dataflow's built-in monitoring tools, autoscaling capabilities, and applying cost optimization strategies.
Key Facts:
- Dataflow provides various metrics (autoscaling, streaming, resource, I/O) accessible via the monitoring interface and Google Cloud Monitoring.
- Autoscaling in Dataflow dynamically adjusts worker instances (horizontal) and memory allocation (vertical) based on job demands.
- Cost optimization involves defining SLOs, monitoring costs, adjusting machine types, and utilizing preemptible workers and Flexible Resource Scheduling (FlexRS).
- Troubleshooting tools include the monitoring interface, Cloud Monitoring, and Cloud Error Reporting to identify performance bottlenecks and errors.
- Metrics are crucial for debugging, performance optimization, and maintaining cost control.
Streaming Data Processing: Windowing and Watermarks
This sub-topic delves into the specialized mechanisms Dataflow employs for robust streaming data processing, specifically focusing on windowing, watermarks, and triggers. These concepts are crucial for handling unbounded datasets, managing out-of-order data arrival, and ensuring accurate aggregations in real-time scenarios.
Key Facts:
- Windowing divides unbounded data streams into finite logical segments based on element timestamps.
- Watermarks indicate Dataflow's expectation of data completeness within a window, critical for managing late-arriving data.
- Triggers determine when aggregated results from a window are emitted, configurable by event time, processing time, or data element count.
- Tumbling, hopping, and session windows are common types of windowing supported by Dataflow.
- These concepts operate based on event time to ensure accurate computations regardless of processing delays.
End-to-End Data Pipeline Integration and Orchestration
This sub-topic focuses on building cohesive end-to-end data solutions by integrating Pub/Sub, Dataflow, and BigQuery, and explores tools for workflow management. It covers common real-time streaming patterns and the orchestration of complex, interdependent data workflows.
Key Facts:
- Building cohesive solutions by connecting BigQuery, Dataflow, and Pub/Sub is a key aspect.
- A common real-time streaming pattern involves ingesting data via Pub/Sub, processing with Dataflow, and loading into BigQuery.
- Workflow orchestration tools like Cloud Composer (managed Apache Airflow) are important for managing complex workflows.
- Hands-on labs focusing on Pub/Sub to Dataflow to BigQuery pipelines are highly beneficial.
- Understanding how these services work together to form a complete data solution is critical.
Best Practices for End-to-End Pipeline Design
This sub-topic outlines best practices for designing robust, scalable, and cost-effective end-to-end data pipelines on GCP. It covers crucial aspects such as modular design, comprehensive monitoring, quota management, and cost optimization, emphasizing the importance of templates and notebooks for accelerated development.
Key Facts:
- Employing a modular and simple design is crucial for pipeline clarity and maintainability.
- Defining and monitoring Service Level Objectives (SLOs) is essential for pipeline performance.
- Awareness and management of service quotas and limits for Pub/Sub, Dataflow, and BigQuery are necessary.
- Cost optimization involves considering real-time vs. batch processing costs and utilizing Dataflow's auto-scaling.
- Leveraging Dataflow templates and Vertex AI notebooks can significantly accelerate pipeline development.
Dataflow for ETL Workloads
This sub-topic delves into Dataflow's role as a fully managed service for Extract, Transform, Load (ETL) workloads, applicable for both batch and streaming data. It explores how Dataflow utilizes Apache Beam for transformations and integrates with other GCP services like BigQuery and Cloud Storage for comprehensive data processing.
Key Facts:
- Dataflow is a fully managed service for ETL workloads, supporting both batch and streaming data processing.
- It uses the Apache Beam SDK, providing a unified programming model for batch and stream processing.
- Dataflow can extract data from sources like Cloud Storage, transform it, and load it into BigQuery.
- Dataflow can orchestrate BigQuery queries, allowing transformations to be performed within BigQuery itself.
- Considerations for ETL pipeline design include data volume, velocity, complexity, governance, scalability, and cost.
Real-time Streaming Patterns on GCP
This sub-topic covers the foundational real-time streaming pattern on GCP, which involves ingesting data via Pub/Sub, processing it with Dataflow, and loading it into BigQuery for immediate analysis. It emphasizes the integration of these core services to achieve dynamic and real-time insights.
Key Facts:
- The common real-time streaming pattern on GCP integrates Pub/Sub for ingestion, Dataflow for processing, and BigQuery for data warehousing.
- Pub/Sub acts as a messaging service, handling millions of real-time messages per second with auto-scaling.
- Dataflow, leveraging Apache Beam, transforms and enriches data in both stream and batch modes, offering auto-scaling and fault-tolerance.
- BigQuery serves as the analytical data warehouse, providing high-performance analysis of large datasets with automatic scaling.
- This pattern enables real-time analytics where data is processed and loaded into BigQuery as soon as it's received.
Workflow Orchestration with Cloud Composer
This sub-topic focuses on workflow orchestration using Cloud Composer, GCP's fully managed Apache Airflow service. It highlights Cloud Composer's importance in managing complex, interdependent data workflows by defining them as Directed Acyclic Graphs (DAGs) and its seamless integration with other GCP services.
Key Facts:
- Cloud Composer is a fully managed workflow orchestration service based on Apache Airflow.
- It enables data engineers to author, schedule, and monitor workflows using Python, defining them as Directed Acyclic Graphs (DAGs).
- Cloud Composer integrates seamlessly with GCP services like BigQuery, Dataflow, and Pub/Sub for end-to-end pipeline orchestration.
- Benefits include centralized workflow management, dependency management, monitoring, and alerting.
- Other GCP services like Cloud Data Fusion, Cloud Scheduler, and Cloud Functions can also contribute to orchestration strategies.
GCP Fundamentals for Data Engineering
This sub-topic covers the essential foundational knowledge of Google Cloud Platform's core infrastructure and data-related services relevant to data engineering. It includes understanding general cloud computing concepts, the GCP environment, and basic services crucial for data professionals.
Key Facts:
- GCP Fundamentals for Data Engineering includes general cloud computing concepts and the GCP environment.
- It covers basic services like Cloud Storage, Identity and Access Management (IAM), and networking basics.
- Proficiency in programming languages like Python and SQL is considered essential for a data engineer.
- Key responsibilities of a data engineer, such as data ingestion, storage, processing, and orchestration, are starting points.
- Understanding data governance and security within GCP is a crucial aspect of foundational knowledge.
Cloud Computing Concepts and GCP Environment
This sub-topic establishes the foundational understanding of general cloud computing principles and introduces the Google Cloud Platform (GCP) as a specific environment for data engineering activities. It covers the core aspects of cloud services that underpin all data operations within GCP.
Key Facts:
- GCP Fundamentals for Data Engineering includes general cloud computing concepts and the GCP environment.
- Cloud computing provides scalable infrastructure and services for data storage and processing.
- Understanding the GCP environment involves familiarization with its global infrastructure and service offerings.
- Core GCP services are used for various data engineering tasks including ingestion, storage, processing, and analysis.
- GCP provides managed services that abstract away underlying infrastructure complexities.
Cloud Storage as a Data Lake Landing Zone
This sub-topic focuses on the practical application of Cloud Storage as a foundational data lake landing zone within GCP environments. It highlights its benefits like cost-effectiveness, scalability, and seamless integration with other GCP data services, enabling the storage of diverse data formats for subsequent processing and analysis.
Key Facts:
- Cloud Storage is frequently used as a data lake landing zone due to its cost-effectiveness and scalability.
- It allows for storing raw, unstructured, semi-structured, and structured data in its native format.
- Cloud Storage integrates seamlessly with other GCP services like BigQuery, Dataflow, and Dataproc.
- Data stored in Cloud Storage can be processed and analyzed using various GCP tools.
- It provides a highly secure and durable object storage solution for data lakes.
Core GCP Data Services
This module delves into the essential GCP services critical for data engineers, focusing on their roles in data ingestion, storage, processing, and analysis. It highlights key offerings such as BigQuery, Cloud Storage, Dataflow, and Pub/Sub, which form the backbone of modern data pipelines on GCP.
Key Facts:
- BigQuery is a serverless, highly scalable data warehouse for analytics, supporting fast SQL queries.
- Cloud Storage is a highly scalable and secure object storage service, acting as a foundational data lake component.
- Dataflow is a fully managed service based on Apache Beam for executing both batch and streaming data processing pipelines.
- Pub/Sub is a real-time messaging service for ingesting and delivering streaming data, enabling event-driven architectures.
- Other relevant services include Cloud SQL for relational databases, Dataproc for Spark/Hadoop, and Cloud Composer for workflow orchestration.
Data Governance and Security in GCP
This module addresses the critical responsibilities of data engineers concerning data governance and security within the GCP ecosystem. It covers ensuring data quality, privacy, accuracy, availability, and compliance with regulations, along with implementing security measures like encryption, access control, and auditing using GCP's specialized services.
Key Facts:
- Data governance ensures data is secure, private, accurate, available, and usable throughout its lifecycle.
- Compliance with internal standards and external regulations (e.g., GDPR, HIPAA) is a key aspect of data governance.
- GCP services like Data Catalog, Cloud DLP, and Dataplex aid in metadata management and data loss prevention.
- Data security involves encryption at rest and in transit, access control via IAM, and compliance with privacy regulations.
- BigQuery automatically encrypts data, and Cloud KMS can be used for managing encryption keys.
Identity and Access Management (IAM) and Networking Basics
This module explores the critical aspects of securing data projects on GCP through Identity and Access Management (IAM) and understanding fundamental networking concepts. It covers controlling permissions to resources, implementing least privilege principles, and basic network configurations for secure and efficient data transfer within the GCP ecosystem.
Key Facts:
- IAM controls who has what permissions to specific GCP resources like projects, datasets, and tables.
- The principle of least privilege, granting only necessary access, is fundamental for IAM security.
- IAM policies should be configured at the highest common level to leverage inheritance effectively.
- Networking basics involve understanding Virtual Private Clouds (VPCs) and hybrid cloud setups.
- Secure data transfer mechanisms are crucial for optimizing GCP's networking capabilities.
Programming Languages and Technical Skills for GCP Data Engineering
This sub-topic covers the indispensable programming languages and technical skills required for a data engineer working with GCP. It emphasizes proficiency in Python and SQL, alongside an understanding of ETL/ELT processes and Linux fundamentals, as crucial for interacting with GCP services and building data pipelines.
Key Facts:
- Proficiency in Python is widely used for automation, data manipulation, and interacting with GCP services.
- SQL is fundamental for querying, analyzing, and transforming structured data, especially with BigQuery.
- Knowledge of Java/Scala is relevant for Apache Beam in Dataflow pipelines.
- Linux fundamentals, including basic commands and scripting, are necessary for cloud environments.
- Expertise in ETL/ELT processes is central to building and optimizing data pipelines.
Practical Application and Certification
This sub-topic emphasizes the importance of hands-on experience through real-world projects and rigorous preparation for the Google Cloud Professional Data Engineer certification. It serves to validate practical skills and consolidate theoretical knowledge.
Key Facts:
- The learning path should be heavily reinforced with practical exercises and Qwiklabs.
- Building real-world data engineering projects, such as ETL pipelines, is crucial.
- The Google Cloud Professional Data Engineer certification validates proficiency in designing and managing data processing systems on GCP.
- Preparation for this exam provides a structured roadmap covering necessary skills and services.
- Recommended resources include official Google Cloud learning paths, Coursera, Pluralsight, and specialized courses.
GCP Data Engineering Exam Domains and Key Services
This sub-topic delves into the specific knowledge domains and essential GCP services that are critical for the Professional Data Engineer certification exam. It highlights the importance of understanding not only individual services but also their interconnections and common data engineering patterns.
Key Facts:
- The certification requires understanding common data engineering patterns like ETL/ELT and real-time streaming analytics.
- A deep understanding of BigQuery, Dataflow, and Pub/Sub is essential, along with Cloud Storage and Dataproc.
- Familiarity with data modeling and system design principles for data processing is crucial.
- Exam preparation aids in tackling interview questions for GCP Data Engineer roles, covering core services and architecture.
Google Cloud Professional Data Engineer Certification Preparation
This sub-topic focuses on the structured preparation required to pass the Google Cloud Professional Data Engineer certification exam. It outlines the exam details, recommended resources, and the scope of knowledge necessary to validate proficiency in designing and managing data processing systems on GCP.
Key Facts:
- The Google Cloud Professional Data Engineer certification validates an individual's ability to design, build, operationalize, secure, and monitor data processing systems on GCP.
- The exam is typically two hours long, consisting of 50-60 multiple-choice and multiple-select questions.
- Preparation involves official Google Cloud learning paths, study guides, practice questions, and hands-on labs.
- A deep understanding of core GCP services like BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Dataproc is essential.
Hands-on Labs and Practice Environments
This sub-topic focuses on gaining practical experience with GCP services through structured hands-on labs and challenge environments. Platforms like Qwiklabs and Google Cloud Skills Boost provide crucial opportunities to build and test data pipelines in a controlled setting.
Key Facts:
- Qwiklabs and Google Cloud Skills Boost offer guided, hands-on labs for practicing with GCP environments.
- These platforms provide a testing environment essential for building and testing data pipelines.
- Practical experience from these labs is considered crucial for exam preparation for the Google Cloud Professional Data Engineer certification.
Real-world Data Engineering Projects on GCP
This sub-topic covers the development of end-to-end data engineering projects on Google Cloud Platform to consolidate theoretical knowledge and acquire practical skills. It involves designing and implementing various data pipelines, leveraging core GCP services.
Key Facts:
- Building end-to-end data engineering projects is vital for consolidating theoretical knowledge and gaining practical skills.
- Projects often involve designing and implementing ETL/ELT pipelines for various data types.
- Examples include streaming data pipelines using Pub/Sub and Dataflow for real-time processing and Data Warehousing with BigQuery.
- Leveraging Python and SQL is fundamental for data retrieval, transformation, and querying in these projects.
Pub/Sub for Messaging and Event Ingestion
This sub-topic focuses on Google Cloud Pub/Sub, a fully managed asynchronous messaging service critical for real-time event data ingestion and reliable communication in decoupled systems. Learners will explore its core concepts and practical application in event-driven architectures.
Key Facts:
- Cloud Pub/Sub is a fully managed asynchronous messaging service.
- It enables reliable communication between independent applications and real-time event data ingestion.
- Key concepts include publishers, topics, subscriptions (push and pull), and subscribers.
- Features like at-least-once delivery, global message routing, and message retention are important.
- Pub/Sub supports decoupled, event-driven systems and is key for ingesting streaming data into GCP pipelines.
Pub/Sub Advanced Features and Best Practices
This module covers advanced features and operational best practices for Pub/Sub, including Dead Letter Topics for handling undeliverable messages, retry policies for message delivery, and considerations for global availability, scalability, and security. It aims to equip learners with knowledge for building production-ready Pub/Sub solutions.
Key Facts:
- Dead Letter Topics (DLT) provide a mechanism to redirect undeliverable messages for reprocessing or debugging.
- Configurable Retry Policies allow for immediate retries or exponential backoff for failed message delivery.
- Pub/Sub is designed for global availability and horizontal scalability, handling millions of messages per second with low latency.
- Security features include high message durability through multiple server copies and encrypted endpoints for secure transit.
- Authentication mechanisms are supported to control access to Pub/Sub topics and subscriptions.
Pub/Sub Core Concepts
This module introduces the foundational concepts of Google Cloud Pub/Sub, detailing the roles of publishers, topics, messages, subscriptions, and subscribers. It establishes the basic framework for understanding how asynchronous communication and message delivery are structured within the service.
Key Facts:
- Publishers are applications that create and send messages to a Pub/Sub topic.
- Topics are named resources that categorize and organize messages, acting as channels for publishers.
- Messages are the units of data exchanged, potentially containing key-value pair attributes.
- Subscriptions are named resources representing a stream of messages from a specific topic, delivered to subscribing applications.
- Subscribers are applications or services that receive and process messages from a subscription.
Pub/Sub for Event-Driven Architectures and Streaming Ingestion
This module focuses on Pub/Sub's role as a cornerstone for building scalable and responsive event-driven systems and its utility in real-time streaming data ingestion. It explores patterns for integrating Pub/Sub with other GCP services like BigQuery, Dataflow, and Cloud Storage for comprehensive data pipelines.
Key Facts:
- Pub/Sub is fundamental for building scalable and loosely coupled event-driven systems on GCP.
- It acts as a robust ingestion service for streaming data, enabling real-time data exchange for applications like fraud detection.
- Pub/Sub integrates natively with GCP services such as BigQuery for warehousing and Dataflow for processing.
- Streaming data ingestion patterns include direct ingestion, Cloud Storage subscriptions for data lakes, and Dataflow for complex transformations.
- Cross-Cloud Ingestion capabilities allow integrating streaming data from external sources like AWS Kinesis Data Streams.
Pub/Sub Message Delivery and Guarantees
This module explores the mechanisms and assurances Pub/Sub provides for message delivery, covering asynchronous communication, 'at-least-once' and 'exactly-once' delivery semantics, message acknowledgment, and delivery types like pull and push subscriptions. It also addresses message ordering considerations.
Key Facts:
- Pub/Sub offers asynchronous communication to decouple services and improve resilience.
- At-Least-Once Delivery guarantees messages are delivered, but subscribers must handle potential duplicates idempotently.
- Exactly-Once Delivery is available for pull subscriptions within the same region, ensuring acknowledgments prevent re-delivery.
- Subscribers acknowledge message receipt; unacknowledged messages are redelivered by Pub/Sub.
- Delivery types include 'pull' where subscribers fetch messages, and 'push' where Pub/Sub sends messages to an HTTP/S endpoint.
Pub/Sub Message Retention Policies
This module delves into Pub/Sub's message retention capabilities, distinguishing between subscription retention and topic retention. It covers how messages can be preserved and replayed, including the 'seek' feature for navigating message history and its implications for data recovery and processing.
Key Facts:
- Subscription Retention allows messages to be kept for a specified duration (default 7 days, max 31 days) within a subscription.
- Acknowledged messages can also be retained in subscriptions, impacting storage costs.
- Topic Retention enables messages to be saved and replayed directly from the topic for up to 31 days, irrespective of subscription creation time.
- The 'Seek' feature allows replaying messages from a specific snapshot or timestamp, or re-processing previously acknowledged messages.
- Topic retention ensures all messages sent within the window are accessible to any subscription, even if created after message publication.